Hacking Human Future

Working on the next big thing in Artificial Intelligence: foundational research in artificial intelligence, multiagent systems, game theory, automated planning, robotics and cybersecurityapplied research translated into building large scale systems and technology transfer by contracted research and building startups.

Game Theory

We focus on online and offline computation of optimal strategies in dynamic domains that evolve in time with finite and infinite horizon. The main research topics include equilibrium computation algorithms, online sampling algorithms, learning in games, and computational complexity of related problems.

Planning

The ability to plan a sequence of actions in order to reach a desired goal is one of the basic manifestation of intelligence, both natural and artificial. It is thus no surprise that planning has been studied in AI since its very beginning in the 60's and 70' with the STRIPS language and the well known A* algorithm. Both are used up to date, but with significant improvement in automated translation and invariant synthesis and automatically derived heuristics exploiting the problem structure. In our group we focus on the research of basic principles of classical planning and heuristics, but also on variants such as multi-agent planning and on the application of planning to real-world problems.

Machine Learning

Internet security: machine learning is the new changing force of general science, and the most important game-changer in security. The dynamic nature of attacks, malware, digital fraud and adversaries can only be matched by complex and accurate machine learning systems. These systems take advantage of our data, game strategies, algorithms and expertise in real attacks to fulfil a broad range of security solutions for both companies and the civil society stratosphere.org

Game theory: We study combination of machine learning and game theory in two main directions. First, we use machine learning in computing strategies for large complex games. Machine learning is used to automatically abstract the most important features of the games and to compute heuristics that improve scalability of our game theoretic algorithms. Second, we use game theory to understand machine learning in adversarial settings. Existing machine learning techniques are very vulnerable to carefully crafted adversarial samples. Network intrusion detection can be easily avoided by modifying attack patterns, visual traffic sign recognition can be confused by small innocent-looking stickers, face recognition can be misled by specially crafted glasses. We believe that game theory is the right framework to study this phenomenon http://aic.fel.cvut.cz/gametheory/

Robotics: In the Computational Robotics Laboratory (ComRob), we are seeking for unique solutions to address real-world challenges to improve quality of life and to understand principles emerging in nature. We are solving problems at the intersections of the artificial intelligence and autonomous robotic systems using traditional computational approaches, but also machine learning techniques. In addition to statistical methods and supervised learning applied, e.g., in terrain classification, locomotion control, and spatiotemporal mapping; we are also working on semi-supervised and unsupervised learning methods in planning and signal processing. Our mission is to develop lifelong learning robotic system that will adapt to environmental changes, improve its performance by incremental learning in long-term autonomous tasks in previously unknown environments that can be populated by humans https://comrob.fel.cvut.cz/

Network Security

Smart Urban Mobility

We are developing models that provide insights into the performance of on-demand transportation systems and use them to estimate the impact of their large-scale deployment. Further, we are looking in the potential of advanced optimization techniques, automation, ride-sharing and market-based mechanisms to achieve affordable, efficient, and congestion-free urban traffic.

Robotics

Expertise

Our know-how and experience runs a wide gamut covering key AI areas as well as several prominent application domains.

Multiagent Systems and Collective AI

Models and algorithms for making teams of physical and/or digital autonomous agents coordinating their activity and collaborate on solving challenging problems, such as in multi-drone surveillance, connected vehicles and/or adaptive logistics.

Automated Planning and Decision Making

Planning and decision making provides general algorithmic solutions to problems of single or repeated action selection in formally modelled environments of various domains. We develop algorithms which combine techniques of dynamic programming, heuristic search and provide efficient and practically usable planning approaches.

Intelligent Data Analysis

Intelligent Data Analysis applies modern AI methods to data mining in order to facilitate and improve knowledge acquisition. Based on semi and fully-automatic approaches, methods of machine learning such as pattern recognition or clustering are typically employed. The process often deals with high-dimensional or complex spatio-temporal data and their visualization.

Algorithmic Game Theory

We focus on online and offline computation of optimal strategies in dynamic domains that evolve in time with finite and infinite horizon. The main research topics include equilibrium computation algorithms, online sampling algorithms, learning in games, and computational complexity of related problems.

Computational Robotics

We aim to design new scalable algorithms and computational models enabling application and improving capabilities of collective robotic systems to operate in dynamic, unstructured environments with imperfect sensing and perception.

Intelligent Transport Systems

We have a deep knowledge and an extensive experience of algorithmic foundations of intelligent transport systems, ranging from next-generation trip planning, through agent-based transport modeling up to auction-based intelligent transport service marketplaces.

Agent-based Simulation Modelling

We explore how transport systems can be modelled as genuinely multi-agent systems, composed of autonomous agents with continuous, asynchronous control modules and the ability to interact freely with the environment and other agents. Such a fully agent-based approach – which is not supported by existing platforms – improves the modularity and extensibility of transport simulations and reduces constraints on the type of decision models that can be simulated (e.g. within-the-day replanning).
We also explore how agent-based simulation models can be used as testbeds for analysing on-demand mobility and logistics services, including real-time ride sharing, transportation network services or next-generation taxi services.

Planning for manufacturing and logistics

The industrial cooperation is one of the most important application areas for us. The goal of this activity is to apply state of the art data engineering practices and algorithmization know-how to real world problems encountered within partner enterprises, including production planning, production scheduling and logistics domain assistive tools. This effort fits well into what is currently perceived as the most feasible way of integrating advanced (decentralized) AI algorithms to industry expert driven applications helping the industry to comply with the Industry 4.0 needs.

Cybersecurity

Our modern world is based on complex applications, networks, social relationships, mobile devices, permanent updates and synergic exponential organizations. The only drawback of our societies are our concerns for security, privacy and anonymity. We need to be secure in a world where every new connection leave us more vulnerable. We believe that science can help our community by leveraging the security risks with powerful and advanced protections.

Team

We are a diverse and balanced team counting 48 students, researchers and academics.

Student Projects

Ph.D. Positions

Foundational research in artificial intelligence, multiagent systems, game theory, automated planning, computational robotics with applications to cybersecurity, next generation transportation systems or intelligent manufacturing. We work on many R&D projects answering fundamental theoretical questions, designing state‐of‐art AI algorithms and implementing cutting‐edge practical AI systems.
The start date is as soon as possible.

Funding:

The position is fully funded. Besides waving/covering the tuition fees, the stipend + salary for a starting PhD student is above the average salary in Prague, offering comfortable living in one of the most vibrant and cosmopolitan capitals in EU.

Location:

Downtown Prague, Czech Republic

Team:

We are young and quickly growing research center at the oldest technical university in the region. We strive to reach artificial intelligence research results that are extraordinary thanks to their revolutionary nature, size, relevance and benefit to science and society.

We are looking for a new PhD student for our team, led by best researchers and academics. If you might be interested, contact us at katarina.takusova@fel.cvut.cz

The application should include:

Structured Curriculum Vitae.

Motivation letter

Post-doc Positions

Artificial Intelligence Center, Department of Computer Science opens a competitive call for applications for a postdoctoral contract in the context of a recently awarded national center of excellence: Research Center for Informatics (RCI).

AIC contributes RCI in foundational research in artificial intelligence, multiagent systems, game theory, automated planning, computational robotics with applications to cybersecurity, next-generation transportation systems or intelligent manufacturing. Besides RCI, we work on many R&D projects answering fundamental theoretical questions, designing state-of-art AI algorithms and implementing cutting-edge practical AI systems. AIC is also focused on AI applications, as a result of which several successful startups have been incubated from within AIC.

Essential qualifications and skills:

Ph.D. in computer science

demonstrable experience in research od AI

good publication record

good interpersonal as well as communication skills

Term of contract:

The duration of the position is 24 months (with possible extension)

Salary:

The position will offer a competitive salary with respect to the costs and quality of life in Prague, one of the most exciting and cosmopolitan Europan capitals.

Location:

Downtown Prague, Czech Republic

Interested applicants are invited to send their application by email to katarina.takusova@fel.cvut.cz with the subject line: „Postdoctoral application at AI Center.”

The application should include:

Structured Curriculum Vitae.

Publication record, including the selection of three most impactful pieces of work by the applicant and detailed explanation of the respective impact.

Our unique mix of blue sky research and hands-on AI system implementation has stimulated the creation of several innovative tech companies.

Cognitive Security

Blindspot Solutions

Development of an intelligent software for both young and well established companies. Blindspot Solutions is helping to solve complex operational problems by utilizing latest trends from AI, machine learning and data analysis.

AgentFly Technologies

Air traffic management simulation for what-if analysis and future concepts. Advanced intelligent planning and control for a group of automated unmanned aerial assets.

Umotional

Established in 2015, Umotional develops next-generation solutions for smart mobility. It currently works on two products: UrbanCyclers -- a smart suite of applications and services that help cities cycle more, and MetaPlan – a unique intermodal trip planning engine that works with APIs rather than data.